The present invention, in some embodiments thereof, relates to creating a classification function for classifying media objects, and, more specifically, but not exclusively, to creating a classification function for classifying media objects by a statistical analysis of features extracted from a plurality of sample media objects.
With the increasing need for recognition and/or detection of captured media objects such as, for example, video streams, images, speech, audio, music and/or hand writing, learning models have become a common practice for classifying the captured media objects. The learning models, such as for example, artificial neural networks (ANN) and/or convolutional neural networks (CNN) are trained with sample data, i.e. sample media objects and continuously evolve (learn) during the process of classifying new (previously unseen) media objects.
To improve generalization and/or avoid overfitting of the learning models the sample data used during the training process may often be subject to data augmentation and/or transformation. Data augmentation aims to increase the sample data base in order to enhance the ability of the learning models to identify, recognize and/or classify invariances of the sample data which is presented in different and/or transformed representations. Data augmentation may also serve to compensate for the limited sample data base available for training the learning models. Data augmentation may present additional major challenges as it may require excessive resources, for example, computation time, storage space and/or computation load.